Significance: The adaptive immune system uses the experience of past infections to prepare its limited repertoire of specialized receptors to protect organisms from future threats. What is the best way of doing this? Building a theoretical framework from first principles, we predict the composition of receptor repertoires that are optimally adapted to minimize the cost of infections from a given pathogenic environment. A naive repertoire can reach these optima through a biologically plausible competitive mechanism. Our findings explain how limited populations of immune receptors can self-organize to provide effective immunity against highly diverse pathogens. Our results also inform the design and interpretation of experiments surveying immune repertoires.

The COVID-19 pandemic is an ongoing global health threat, yet our understanding of the dynamics of early cellular responses to this disease remains limited¹. Here in our SARS-CoV-2 human challenge study, we used single-cell multi-omics profiling of nasopharyngeal swabs and blood to temporally resolve abortive, transient and sustained infections in seronegative individuals challenged with pre-Alpha SARS-CoV-2. Our analyses revealed rapid changes in cell-type proportions and dozens of highly dynamic cellular response states in epithelial and immune cells associated with specific time points and infection status. We observed that the interferon response in blood preceded the nasopharyngeal response. Moreover, nasopharyngeal immune infiltration occurred early in samples from individuals with only transient infection and later in samples from individuals with sustained infection. High expression of HLA-DQA2 before inoculation was associated with preventing sustained infection. Ciliated cells showed multiple immune responses and were most permissive for viral replication, whereas nasopharyngeal T cells and macrophages were infected non-productively. We resolved 54 T cell states, including acutely activated T cells that clonally expanded while carrying convergent SARS-CoV-2 motifs. Our new computational pipeline Cell2TCR identifies activated antigen-responding T cells based on a gene expression signature and clusters these into clonotype groups and motifs. Overall, our detailed time series data can serve as a Rosetta stone for epithelial and immune cell responses and reveals early dynamic responses associated with protection against infection.

Significance: Organisms possess many mechanisms for protecting themselves from pathogens. In addition to the well-studied innate and adaptive immune systems of vertebrates, recently discovered mechanisms such as CRISPR immunity in prokaryotes diversify the known modes of information processing used in immune protection. By classifying these different systems in terms of their rules of heritability and response to the environment, we propose a mathematical framework that recapitulates the observed natural diversity of immune systems. We show how the basic modes of immunity emerge as optimal strategies of a population adapting to a changing pathogenic environment. The proposed framework offers a unified view of immunity across species and helps rationalize the diversity of observed mechanisms.

Significance: Antigen-specific T cells proliferate multiple times during an immune reaction to fight against disease. This expansion of T cells must be carefully regulated to ensure an effective defense while avoiding autoimmunity. One challenge to regulation is that the initial size of antigen-specific T cell clones can be quite variable. Intriguingly, a recent study in mice found that the fold expansion of a T cell clone depends as an inverse power law on its initial size. We propose a simple mathematical model that naturally yields the observed power law relation. Our model accounts for multiple experiments on T cell proliferation, suggests optimal vaccination protocols, and highlights dynamics of presented antigen as a key regulator of the size of an immune response.

Significance: The adaptive immune system is able to protect us from a large variety of pathogens, even ones it has not seen yet. Can predicting the future pathogen distribution help in protection? We find that a combination of probabilistic forecasting and occasional sampling of the current environment reduces infection costs—a scheme easily implemented by the memory repertoire. The proposed theoretical framework offers a modular recipe for updating the memory repertoire, which quantitatively predicts the strength of the immune response in flu-vaccination experiments, unlike other update schemes. It also links the observed early life dynamics of the memory pool to the sparseness properties of the pathogen distribution and competitive receptor dynamics for pathogens.